Long-term cropping and fertilization influences soil organic carbon, soil water repellency, and soil hydrophobicity
Bibliographic record
Abstract
Long-term (58 yr) cropping and fertilization effects on soil water repellency were determined for a clay loam soil in southwestern Ontario, Canada by measuring soil organic carbon (SOC), soil water repellency index (RI), and soil hydrophobicity (SH). The 12 treatments (non-replicated) included fertilized and non-fertilized legume-based crop rotation (ROT) with four phases (corn–oat–alfalfa–alfalfa), continuous corn (CC), and continuous Kentucky bluegrass (KBG). We hypothesized that SOC, RI, and SH would be greater for each phase of the ROT versus CC, KBG versus CC and ROT, and fertilized versus non-fertilized treatments. Surface (0–10 cm) soil samples were collected in the spring of 2017. Laboratory measurements were conducted to determine SOC, RI (ratio of soil sorptivity to ethanol and water), and SH (ratio of hydrophobic CH– to hydrophilic CO– functional groups). Mean SOC and SH were greater (P ≤ 0.05) for each phase of the ROT versus CC (33% to 2.4 times), KBG versus CC (3.2–6 times) and each phase of ROT (2.2–2.8 times), and fertilized versus non-fertilized rotation oats and KBG (15%–30%). Mean RI was greater for KBG versus CC (4.8 times) and KBG versus each phase of the ROT (3.0–5.5 times) under fertilization only, greater for fertilized versus non-fertilized KBG (6.8 times), but similar for each phase of ROT versus CC. In general, legume-based rotations, perennial grass, and fertilizer enhanced SOC and SH, and to a lesser extent soil RI.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".